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We propose a new method for learning with multi-field categorical data.Multi-field categorical data are usually collected over many heterogeneousgroups. These groups can reflect in the categories under a field. The existingmethods try to learn a universal model that fits all data, which is challengingand inevitably results in learning a complex model. In contrast, we propose afield-wise learning method leveraging the natural structure of data to learnsimple yet efficient one-to-one field-focused models with appropriateconstraints. In doing this, the models can be fitted to each category and thuscan better capture the underlying differences in data. We present a model thatutilizes linear models with variance and low-rank constraints, to help itgeneralize better and reduce the number of parameters. The model is alsointerpretable in a field-wise manner. As the dimensionality of multi-fieldcategorical data can be very high, the models applied to such data are mostlyover-parameterized. Our theoretical analysis can potentially explain the effectof over-parametrization on the generalization of our model. It also supportsthe variance constraints in the learning objective. The experiment results ontwo large-scale datasets show the superior performance of our model, the trendof the generalization error bound, and the interpretability of learningoutcomes. Our code is available athttps://github.com/lzb5600/Field-wise-Learning.

Field-wise Learning for Multi-field Categorical Data